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作 者:Leilei Zhang Guoxin Wang Weijian Sun
机构地区:[1]State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian,People's Republic of China [2]Institute of Earthquake Engineering,Faculty of Infrastructure Engineering,Dalian University of Technology,Dalian,People's Republic of China
出 处:《International Journal of Digital Earth》2023年第1期3348-3367,共20页国际数字地球学报(英文)
基 金:supported by the National Key Research and Development Program of China under Grant number[2018YFD1100405].
摘 要:Building structural type information is the foundation for seismic risk assessment and management since it reflects the behavior of buildings under seismic load.However,in earthquake-prone regions,most of this information is out-of-date or nonexistent.This paper proposes a deep learning-based method for automatically identifying building structural types from unmanned aerial vehicle(UAV)oblique images.The method consists of four steps:(1)collect facades of buildings with different structural types by web crawler technology as a sample set;(2)construct a convolutional neural network with a facade prior knowledge attention branch and train the model using the sample set;(3)extract building facades from UAV oblique images based on the georeferencing results of feature points as the test set;(4)identify building structural types by inputting the test set into the trained model.Three cases have been selected to verify the feasibility and applicability of the method.The average recall rate of 85%and the average F1 score of 83%have been achieved in areas with regular building distribution.This method integrates multidisciplinary knowledge to provide a solution for rapid collection of building vulnerability information,and expands the role of oblique photography data in urban management and disaster prevention planning.
关 键 词:Oblique photography deep learning building structural types UAV attention branch
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]
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